Grid4c

Eran Samuni

Eran Cohen

Alexander Zak

Noa Rimini

Nathaniel Shimoni

Noa Ruschin Rimini

A system for automatic detection of inefficient household thermal insulation includes a server module and a plurality of household client modules. The system performs the following steps: acquiring data relating to each monitored household; identifying periods of HVAC down-time and determining indoor temperature gained during these periods; extracting parameters of temperature gain, relating to the measured rate of temperature gain during the down time; training a machine learning algorithm, to create at least one classification model, wherein all monitored households are classified according to the parameters of temperature gain; producing expected values for parameters of temperature gain per each household, according to household's class membership; producing the ratio between the expected and measured values for parameters of temperature gain per each monitored household; comparing the ratio among similar households; and identifying inefficiently insulated household according to the comparison.

Method and system for automatic detection of inefficient household thermal insulation

Systems and methods are provided for predicting inefficient HVAC operation, by obtaining first training data for HVACs in a training set of households during a first period of moderate weather; obtaining second training data for HVACs in the training set of households during a subsequent period of harsher weather; generating classification labels of the household locations of the training set according to the second training data; applying the first training data and the classification labels to train a supervised machine learning algorithm, to generate an HVAC classification model predictive of inefficiency during periods of harsher weather conditions; obtaining operational data pertaining to HVACs in an operational set of households during a second period of moderate weather; and applying the HVAC classification model to predict inefficiency of HVACs at individual households in the operational set during a second subsequent period of harsher weather.

Methods and systems for HVAC inefficiency prediction

The present invention provides a method for determining conditions of malfunction or inefficiency of HVAC systems, within a plurality of monitored households, in which there are no sensors for directly measuring the power consumption per specific HVAC. The said method comprise the steps of: a. monitoring the power consumption of a plurality of households; b. monitoring the concurrent environmental conditions at the location of the said plurality of households; c. analyzing each household's power consumption, and extracting weighted failure indications of inefficient or malfunctioning HVAC systems; d. determining the probability of various HVAC conditions of malfunction of inefficiency, according to the said weighted indicators; and e. emitting a an alert in relation to the said condition of HVAC malfunction of inefficiency, comprising at least one of: HVAC malfunction type, probability, probable cause, and suggested action.

Method and system for HVAC malfunction and inefficiency detection over smart meters data

A method for monitoring heating, ventilation, and air conditioning (HVAC) systems includes: Obtaining first training data for HVACs in a training set of households during a first period of spring weather; Obtaining second training data during a period of summer weather, Preprocessing the training data to identify repeating patterns of HVAC consumption or generating additional derived parameters, in an aggregation process; Calculating the amount of energy required to change house temperature; Applying the first training data and the classification labels to train a supervised machine learning algorithm, to generate an HVAC classification model predictive of inefficiency during periods of summer weather conditions; Obtaining operational data pertaining to HVACs in an operational set of households during a second period of spring weather; and Applying the HVAC classification model to predict inefficiency of HVACs at individual households in the operational set, during periods of summer weather using only overall household power consumption.

Method and system for HVAC inefficiency prediction using household electrical smart meter data

The present invention provides a method for forecasting load and managing a control plan for households having electric appliances wherein the control plan determines the activation of the electric appliances at pre-defined control periods. The method comprises the steps of: pre-processing per meter of households historical consumption of electric appliances at control period in relation to time dependent environmental parameters and household profiles and control program parameters, creating forecast model of consumption of each controlled appliance during next control plan period based on said pre-processing enabling to simulate control program parameters according to predefined goals parameter including at least target cost or consumption, determining control plan parameters for incoming control period, based on forecast models using defined goal parameters and sending control instructions to each group member control module based on determined control plan parameters, time dependent parameters and measured environmental parameters within the household.

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Givatayim, Israel

Eran Samuni